Machine Learning Engineer
Core
Applying machine learning and data-driven techniques to improve the performance, efficiency, and adaptability of advanced MIMO radios and wireless networking systems in dynamic RF environments.
Role type
Machine Learning Engineer (Wireless Communications)
Builds
ML-driven features for Silvus' MANET radios and proprietary MN-MIMO waveform
Domain
Defense, law enforcement, public safety; Wireless communications, MIMO, MANET
Deliverable
production ML models
Required skills
Supervised and unsupervised learning, statistical modeling, Python ML frameworks (TensorFlow, PyTorch, scikit-learn), RF dataset analysis, software prototyping, data pipeline design
Preferred skills
RF signal classification, anomaly detection, spectrum monitoring, MATLAB/C++ for signal processing, embedded ML, adaptive modulation, beamforming, cognitive radio, 3GPP/IEEE standards, GPU acceleration
Technologies
TensorFlow, PyTorch, scikit-learn, MATLAB, C/C++, Python
Responsibilities
Research, design, and implement ML algorithms for link adaptation, interference mitigation, anomaly detection, and spectrum sensing; Analyze real-world RF datasets to extract insights and develop predictive models; Develop software prototypes and integrate ML algorithms with radio firmware and networking stack; Collaborate with cross-functional teams to define ML use cases and evaluate deployed models; Contribute to the design of data pipelines and infrastructure for training, testing, and validating models; Participate in performance benchmarking and iterative improvement cycles
Seniority
Mid-level (2+ years experience)
